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<header><h1><a href="/">ccv</a></h1>
<p>A Modern Computer Vision Library</p>
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<section><h1>serve/*.c</h1>
<h2 id="bbfdetectobjects">/bbf/detect.objects</h2>

<p>Using <a href="/doc/doc-bbf">BBF</a> classifier cascade to detect objects in a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
  <li><strong>‘model’</strong>: what object, for now, it only supports ‘face’.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-bbf/#ccvbbfparamt">ccv_bbf.c</a>.</p>

<p>Supported methods: GET, POST</p>

<h2 id="convnetclassify">/convnet/classify</h2>

<p>Using <a href="/doc/doc-convnet">ConvNet</a> to categorize a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
  <li><strong>‘model’</strong>: what category set, for now it supports ‘image-net-2012’ and ‘image-net-2012-vgg-d’.</li>
  <li><strong>‘top’</strong>: the number of results returned, order by confidence score.</li>
</ul>

<p>Supported methods: GET, POST</p>

<h2 id="dpmdetectobjects">/dpm/detect.objects</h2>

<p>Using <a href="/doc/doc-dpm">DPM</a> mixture model to detect objects in a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
  <li><strong>‘model’</strong>: what object, it now supports ‘pedestrian’ and ‘car’.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-dpm/#ccvdpmparamt">ccv_dpm.c</a>.</p>

<p>Supported methods: GET, POST</p>

<h2 id="icfdetectobjects">/icf/detect.objects</h2>

<p>Using <a href="/doc/doc-icf">ICF</a> classifier cascade to detect objects in a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
  <li><strong>‘model’</strong>: what object, for now, it only supports ‘pedestrian’.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-icf/#ccvicfparamt">ccv_icf.c</a>.</p>

<p>Supported methods: GET, POST</p>

<h2 id="scddetectobjects">/scd/detect.objects</h2>

<p>Using <a href="/doc/doc-scd">SCD</a> classifier cascade to detect objects in a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
  <li><strong>‘model’</strong>: what object, for now, it only supports ‘face’.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-scd/#ccvscdparamt">ccv_scd.c</a>.</p>

<h2 id="swtdetectwords">/swt/detect.words</h2>

<p>Using <a href="/doc/doc-swt">SWT</a> to detect words / texts in a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-swt/#ccvswtparamt">ccv_swt.c</a>.</p>

<p>Supported methods: GET, POST</p>

<h2 id="tldtrackobject">/tld/track.object</h2>

<p>Create a new <a href="/doc/doc-tld">TLD</a> tracking instance with the initial frame.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the initial frame image.</li>
  <li><strong>‘x’</strong>: the initial tracking rectangle’s top left coordinate.</li>
  <li><strong>‘y’</strong>: the initial tracking rectangle’s top left coordinate.</li>
  <li><strong>‘width’</strong>: the initial tracking rectangle’s width.</li>
  <li><strong>‘height’</strong>: the initial tracking rectangle’s height.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-tld/#ccvtldparamt">ccv_tld.c</a>.</p>

<p>Supported methods: GET, POST</p>

<p>On success, it will return the new tracking instance with ‘Location’ header, you can also find its ID in response[‘tld’].</p>

<h2 id="tldtrackobjectd">/tld/track.object/[\d+]</h2>

<p>Continue a <a href="/doc/doc-tld">TLD</a> tracking instance with follow up frames.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the next frame image.</li>
  <li><strong>‘previous’</strong>: the previous frame image, please make sure this is the exact copy of the frame you previous provided, otherwise API will return ‘false’.</li>
</ul>

<p>Supported methods: GET, POST, DELETE</p>

<p>Please make sure that you DELETE the TLD tracking instance once you are done, otherwise the HTTP server cannot reclaim the memory it occupies.</p>

<h2 id="sift">/sift</h2>

<p>Run <a href="/doc/doc-sift">SIFT</a> feature point extraction on a given image.</p>

<ul>
  <li><strong>‘source’ or HTTP body</strong>: the image.</li>
</ul>

<p>You can look up the rest of parameters at <a href="/lib/ccv-sift/#ccvsiftparamt">ccv_sift.c</a>.</p>

<p>Supported methods: GET, POST</p>

<h3><a href="/">&lsaquo;&nbsp;&nbsp;back&nbsp;</a></h3>
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